To ensure environment friendly products in the international supply chain scenario, an important initiative is reverse supply chain (RSC). The benefits (environmental and financial) from a RSC are influenced by disposal of reusable parts, cost factors and emissions during transportation, collection, recovery facilities, recycling, disassembly and remanufacturing. During designing a network for reverse supply chain, some objectives related to social, economic and ecological concerns are to be considered. This paper suggests two strategies for reducing the costs and emissions in a network of RSC. This research work considers design of RSC for a used-car resale company. First strategy outlines the design of a mobile robot—solar-powered automated guided vehicle (AGV) for reducing logistic cost and greenhouse gas (GHG) emissions. The second strategy proposes a new multi-objective optimization model to reduce the costs and emissions of GHG. Strict carbon caps constraint is used as a guideline for reducing emissions. The proposed strategies are tested for a real-world problem at Maruti True Value network design in Tamil Nadu and Puducherry region of India. Two algorithms namely Elitist Nondominated Sorting Genetic Algorithm (NSGA-II) and Heterogeneous Multi-Objective Differential Evolution algorithm (HMODE) are proposed. HMODE is a new improved multi-objective optimization algorithm. To select the best optimal solution from the Pareto-optimal front, normalized weighted objective functions (NWOF) method is used. The strength or weakness of a Pareto-optimal front is evaluated by the metrics namely ratio of non-dominated individuals (RNI) and solution spread measure (SSM). Also, Algorithm Effort (AE) and Optimiser Overhead (OO) are utilized to find the computational effort of multi-objective optimization algorithms. Results proved that proposed strategies are worth enough to reduce the GHG emissions and costs.

中文翻译：

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二手车转售企业绿色供应链管理的移动机器人及进化优化算法

为确保国际供应链场景中的环保产品，一项重要举措是逆向供应链（RSC）。RSC 的收益（环境和财务）受可重复使用部件的处置、成本因素和运输、收集、回收设施、回收、拆卸和再制造过程中排放的影响。在设计逆向供应链网络时，需要考虑与社会、经济和生态问题相关的一些目标。本文提出了两种降低 RSC 网络成本和排放的策略。这项研究工作考虑了二手车转售公司的 RSC 设计。第一个策略概述了移动机器人的设计——太阳能驱动的自动导引车 (AGV)，用于降低物流成本和温室气体 (GHG) 排放。第二个策略提出了一种新的多目标优化模型来降低温室气体的成本和排放。严格的碳上限约束被用作减少排放的指南。提议的策略在印度泰米尔纳德邦和本地治里地区的 Maruti True Value 网络设计中针对实际问题进行了测试。提出了两种算法，即精英非支配排序遗传算法（NSGA-II）和异构多目标差分进化算法（HMODE）。HMODE 是一种新的改进的多目标优化算法。为了从帕累托最优前沿中选择最佳最优解，使用归一化加权目标函数 (NWOF) 方法。帕累托最优前沿的优势或劣势通过非支配个体比率 (RNI) 和解决方案传播度量 (SSM) 等指标进行评估。此外，算法工作量 (AE) 和优化器开销 (OO) 用于查找多目标优化算法的计算工作量。结果证明，所提出的策略足以减少温室气体排放和成本。